model.load_state_dict(
torch.load("./model_train_relation_vse_type_cond_scales.pth"))
criterion = nn.BCELoss()
# Compatibility AUC test
model.eval()
total_loss = 0
outputs = []
targets = []
for batch_num, batch in enumerate(test_loader, 1):
print("\r#{}".format(batch_num), end="", flush=True)
lengths, images, names, offsets, set_ids, labels, is_compat = batch
images = images.to(device)
target = is_compat.float().to(device)
with torch.no_grad():
output, _, _, _ = model._compute_score(images)
output = output.squeeze(dim=1)
loss = criterion(output, target)
total_loss += loss.item()
outputs.append(output)
targets.append(target)
print()
print("Test Loss: {:.4f}".format(total_loss / batch_num))
outputs = torch.cat(outputs).cpu().data.numpy()
targets = torch.cat(targets).cpu().data.numpy()
print("AUC: {:.4f}".format(metrics.roc_auc_score(targets, outputs)))
# Fill in the blank evaluation
is_correct = []
for i in range(len(test_dataset)):
print("\r#{}".format(i), end="", flush=True)
need_rep=True,
vocabulary=len(train_dataset.vocabulary)).to(device)
model.load_state_dict(torch.load("./model_train.pth"))
print("Successfully load model weight...")
model.eval()
criterion = nn.BCELoss()
# Compatibility AUC test
total_loss = 0
outputs = []
targets = []
for batch in tqdm(test_loader):
lengths, batch_g, names, offsets, set_ids, labels, is_compat = batch
target = is_compat.float().to(device)
with torch.no_grad():
output, _, _ = model._compute_score(batch_g)
output = output.squeeze(dim=1)
loss = criterion(output, target)
total_loss += loss.item()
outputs.append(output)
targets.append(target)
print()
print("Test Loss: {:.4f}".format(total_loss / len(test_loader)))
outputs = torch.cat(outputs).cpu().data.numpy()
targets = torch.cat(targets).cpu().data.numpy()
print("AUC: {:.4f}".format(metrics.roc_auc_score(targets, outputs)))
def img2graph(items):
l = items.shape[0]
g = dgl.DGLGraph()